Gaze based classroom notes generator

ABSTRACT

Techniques are provided for creating presentation notes based upon gaze tracking information associated with observers of a presentation. In one example, a computer-implemented method comprises: obtaining, by a system operatively coupled to a processor, gaze information associated with observers of a presentation; determining, by the system, respective content clarity scores for content elements of the presentation based on a content clarity function; selecting, by the system, respective content from one or more content sources for the content elements based on the respective content clarity scores; and generating, by the system, presentation notes based on the presentation and the selected respective content for the content elements of the presentation.

BACKGROUND

The subject invention relates generally to creating presentation notesbased upon gaze tracking information associated with observers of apresentation.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. One or more embodimentsdescribed herein include a system, computer-implemented method, and/orcomputer program product, in accordance with the present invention.

An exemplary system embodiment comprises a memory that stores computerexecutable components; and a processor that executes the computerexecutable components stored in the memory. The computer executablecomponents can include: a gaze tracking component that obtains gazeinformation associated with observers of a presentation; a contentrecommendation component that: determines respective content clarityscores for content elements of the presentation based on a contentclarity function; and selects respective content from one or morecontent sources for the content elements based on the respective contentclarity scores; and a notes generation component that generatespresentation notes based on the presentation and the selected respectivecontent for the content elements of the presentation.

Other embodiments include a computer-implemented method and a computerprogram product.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example, non-limiting system in accordance withone or more embodiments of the present invention.

FIG. 2 illustrates an example, non-limiting, system component inaccordance with one or more embodiments of the present invention.

FIG. 3 illustrates an example, non-limiting, presentation in accordancewith one or more embodiments of the present invention.

FIG. 4 illustrates an example, non-limiting, slide in accordance withone or more embodiments of the present invention.

FIG. 5 illustrates an example, non-limiting, slide with gaze points inaccordance with one or more embodiments of the present invention.

FIG. 6 illustrates an example, non-limiting, slide with fixation areasin accordance with one or more embodiments of the present invention.

FIG. 7 illustrates an example, non-limiting, slide with saccade pathsidentified in accordance with one or more embodiments of the presentinvention.

FIG. 8 illustrates an example, non-limiting, computer-implemented methodin accordance with one or more embodiments of the present invention.

FIG. 9 illustrates another exemplary, non-limiting computer-implementedmethod in accordance with one or more embodiments of the presentinvention.

FIG. 10 illustrates an example, non-limiting operating environment inaccordance with one or more embodiments of the present invention.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be limited by any express orimplied information presented in the preceding Background or Summarysections, and/or in the following detailed description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however in various cases, that the one or more embodiments canbe practiced without these specific details.

Notetaking during presentations and/or consumption of content can be anessential learning activity, helping store and reinforce informationtaught during the presentation or otherwise consumed by an observer ofthe content. A few non-limiting examples of a presentation include aclassroom presentation, a lecture presentation, a training seminar, etc.Notes can serve as a guide to information in the presentation, course,books, and/or other material. Notetaking can be an observer's (e.g.,student, lecture attendee, participant, or any other suitable observerof a presentation and/or content) synopsis of the presentation, a toolfor recalling important points regarding the presentation, and/or areflection of an observer's understanding of the presentation. However,presentations can leave little time for an observer to take notes.Further, with large presentation forums (e.g., large classrooms, largelecture halls, or any other suitable presentation forum) and shortpresentation times, a presentation session might not be able to connectthe presentation content elements together and/or with associatedcontent. Such associated content can include a variety of associatedlearning material (e.g., books, articles, white papers, massive openonline course (MOOC) video, animation, multimedia, audio recordings,and/or other associated content).

By way of overview and example only, one or more exemplary embodimentsof the invention can automatically generate notes, based on gazetracking information associated with observers of a presentation, andmetadata and heuristics associated with the presentation. For example,meta data and heuristics can be obtained for a presentation from asource associated with the presentation, such as from a presenter,teacher, lecturer, author of the presentation, knowledge base, contentdatabase, or any other suitable source associated with the presentation.As a presentation is presented to a set of observers, gaze trackinginformation associated with observers can be obtained. Based on the gazetracking information, respective attentions scores are generated forcontent elements of the presentation. Using the metadata, heuristics,and attention scores, respective content clarity levels are determinedfor the content elements of the presentation. Based on the respectivecontent clarity levels, respective content is selected for the contentelements of the presentation, and presentation notes are generated forthe presentation using the respective selected content and the contentelements of the presentation.

By way of overview and example only, eye gaze estimation can refer todetecting a point (e.g., gaze point) in a given coordinate space atwhich an observer (e.g., such as a human or animal) is looking. Forexample, a camera can capture an image of a head, and using 3D landmarks(e.g., facial or head landmarks) and a determination of a pose of an eyeand/or head, a gaze vector associated with the eye and/or head can beestimated. Eye gaze tracking can refer to detecting respective points(e.g., gaze points) in a given space at which the observer is lookingover time.

It is to be appreciated that some embodiments disclosed herein canautomatically generate presentation notes for a group of observers of apresentation and/or for an individual observer of the presentation. Forexample, presentation notes can be generated based on gaze trackinginformation aggregated for a plurality of observers of the presentation.In another example, presentation notes can be generated based on gazetracking information for a single observer (e.g., where the presentationis presented to the single observer or where the presentation ispresented to a plurality of observers that include the single observer)of the presentation.

For illustration purposes only, examples presented below will discussgenerating presentation notes for a group of observers of apresentation.

One or more embodiments of the subject invention are directed tocomputer processing systems, computer apparatus, computer-implementedmethods, and/or computer program products that facilitate efficiently,effectively, and automatically (e.g., without direct human involvement)generating presentation notes based upon gaze tracking informationassociated with observers of a presentation, and metadata and heuristicsassociated with the presentation. Such computer processing systems,computer-implemented methods, computer apparatus and/or computer programproducts can employ hardware and/or software to solve problems that arehighly technical in nature (e.g., adapted to generate presentation notesbased upon gaze tracking information associated with observers of apresentation, and metadata and heuristics associated with thepresentation) that are not abstract and that cannot be performed as aset of mental acts by a human. For example, one or many humans, cannotefficiently, accurately and effectively manually gather and analyzethousands of data elements related to performing gaze tracking for agroup of observers of a presentation in real-time from one or more livestreams (e.g., series, sequence) of captured images in a real-timenetwork based computing environment, and generate presentation notesbased upon gaze tracking information associated with the observers of apresentation, and metadata and heuristics associated with thepresentation in a timeframe that would be useful to a classroom ofstudents for learning purposes during a grading period. One or moreembodiments of the subject computer processing systems, methods,apparatuses and/or computer program products can enable the automatedreal-time, gaze tracking for a group of observers of a presentation froma live stream of captured images, and automated generation ofpresentation notes based upon gaze tracking information associated withthe observers of a presentation, and metadata and heuristics associatedwith the presentation. By employing automated real-time, gaze trackingfor a group of observers of a presentation from a live stream ofcaptured images, and automatically generating presentation notes basedupon gaze tracking information associated with the observers of apresentation, and metadata and heuristics associated with thepresentation, the processing time and/or accuracy associated with theexisting automated presentation notes generation is substantiallyimproved.

Additionally, the invention includes technical features related totechnological advancements in real-time gaze tracking based automaticpresentation notes generation that have not been previously addressed inthis manner. Further, one or more embodiments of the subject techniquescan facilitate improved performance of automated presentation notesgeneration, that provides for more efficient usage of storage resources,processing resources, and network bandwidth resources to provide highlygranular and accurate real-time gaze tracking based presentation notesgeneration for observers of a presentation from a live stream ofcaptured images. For example, by providing accurate presentation notesbased on gaze tracking information from a live stream of capturedimages, wasted usage of processing, storage, and network bandwidthresources can be avoided by mitigating the need for extra electroniccommunication between observers to exchange presentation notes, and/orbetween observers and presenters get clarify portions of thepresentation.

By way of overview, aspects of systems, apparatuses or processes inaccordance with the present invention can be implemented asmachine-executable component(s) (software) embodied within machine(s),e.g., embodied in one or more computer readable mediums (or media)associated with one or more machines. Such component(s), when executedby the one or more machines, e.g., computer(s), computing device(s),virtual machine(s), etc. can cause the machine(s) to perform theoperations described.

FIG. 1 illustrates an example, non-limiting system 100 in accordancewith one or more embodiments of the present invention. Repetitivedescription of like elements employed in one or more embodimentsdescribed herein is omitted for sake of brevity.

As shown in FIG. 1, the system 100 can include a computing device 102,one or more networks 112 and one or more cameras 114. Camera(s) 114 canbe any camera that can create and/or capture one or more (e.g., a streamof) images in a suitable (visible, infra-red, ultra-violet, etc.) rangeof the spectrum. A few, non-limiting, examples of a camera include: amonocular camera, a stereo camera, a video camera, a laser-based camera,or any other suitable type of camera.

Computing device 102 can include a presentation notes component 104 thatcan facilitate generating presentation notes based upon gaze trackinginformation associated with observers of a presentation, and metadataand heuristics associated with the presentation. Examples, andadditional aspects/details of presentation notes component 104 will bediscussed below.

Referring again to FIG. 1, computing device 102 can also include orotherwise be associated with at least one memory 108 that can storecomputer executable components. By way of example only, such computerexecutable components can include, but are not limited to, presentationnotes component 104 and associated components, and can store datagenerated by such presentation notes component 104 and/or associatedcomponents. Computing device 102 can also include or otherwise beassociated with at least one processor 106 that executes the computerexecutable components stored in memory 108. Computing device 102 canfurther include a system bus 110 that can couple the various computingdevice 102 components including, but not limited to, the presentationnotes component 104, memory 108 and/or processor 106. Although camera(s)114 is depicted as separate from computing device 102, in someembodiments, camera(s) 114 can be part of computing device 102 andconnected via system bus 110.

While a single computing device 102 is shown in FIG. 1, in someembodiments, any number of different types of devices can be associatedwith or include one or more components of computing device 102, e.g.,include part or all of presentation notes component 104. For example, adevice such as camera 114 can include all or some of the components ofpresentation notes component 104. All such embodiments are envisaged.

Computing device 102 can be any computing device that can becommunicatively coupled to and/or include one or more cameras 114,non-limiting examples of which can include, but are not limited to, awearable device or a non-wearable device. Wearable device can include,for example, heads-up display glasses, a monocle, eyeglasses, contactlens, sunglasses, a headset, a visor, a cap, a mask, a headband,clothing, or any other suitable device that can be worn by a human ornon-human user. Non-wearable devices can include, for example, a mobiledevice, a mobile phone, a camera, a camcorder, a video camera, laptopcomputer, tablet device, desktop computer, server system, cable set topbox, satellite set top box, cable modem, television set, monitor, mediaextender device, blu-ray device, DVD (digital versatile disc or digitalvideo disc) device, compact disc device, video game system, portablevideo game console, audio/video receiver, radio device, portable musicplayer, navigation system, car stereo, a mainframe computer, a roboticdevice, a wearable computer, an artificial intelligence system, anetwork storage device, a communication device, a web server device, anetwork switching device, a network routing device, a gateway device, anetwork hub device, a network bridge device, a control system, or anyother suitable computing device 102. It is to be appreciated thatcomputing device 102 and/or camera 114 can be equipped withcommunication components (not shown) that enable communication betweencomputing device 102 and/or camera 114 over one or more networks 112.

While embodiments disclosed herein refer to one or more live streams ofimages from one or more cameras 114, some embodiments can use one ormore stored images.

The various devices (e.g., computing device 102, cameras 114) andcomponents (e.g., presentation notes component 104, memory 108,processor 106 and/or other components) of system 100 can be connectedeither directly or via one or more networks 112. Such networks 112 caninclude wired and wireless networks, including, but not limited to, acellular network, a wide area network (WAN) (e.g., the Internet), or alocal area network (LAN), non-limiting examples of which includecellular, WAN, wireless fidelity (Wi-Fi), Wi-Max, WLAN, radiocommunication, microwave communication, satellite communication, opticalcommunication, sonic communication, or any other suitable communicationtechnology.

FIG. 2 illustrates an example, non-limiting, presentation notescomponent 104 in accordance with one or more embodiments of the presentinvention. Repetitive description of like elements employed in one ormore embodiments described herein is omitted for sake of brevity.

In one or more embodiments, the presentation notes component 104 canautomatically generate presentation notes based upon gaze trackinginformation associated with observers of a presentation, and metadataand heuristics associated with the presentation. Presentation notescomponent 104 can include content component 202, gaze tracking component204, attention scoring component 206, content recommendation component208, and notes generation component 210.

Content component 202 can obtain a presentation, and metadata andheuristics associated with the presentation. Exemplary presentationswill be discussed in more detail below, with reference to FIGS. 3-7.Gaze tracking component 204 can generate gaze tracking information forone or more observers of a presentation, based on one or more imagescaptured from one or more cameras 114 (FIG. 1) directed at theobserver(s) during the presentation. In some embodiments, gaze trackingcomponent 204 can includes time stamped gaze points on presentationcontent (e.g., one or more slides) with unique observer identifierscorresponding to respective observers. An example of gaze points will bediscussed in more detail with respect to FIG. 5. Attention scoringcomponent 206 can determine respective attentions scores for contentelements of a presentation 302, such using gaze tracking information, aswill be discussed in more detail below. Content recommendation component208 can determine content, from one or more content sources, forassociation with content elements and to include in presentation notes,as discussed in more detail below. Notes generation component 210 cangenerate presentation notes for a presentation 302 using respectivecontent selected for content elements of presentation 302, as will alsobe discussed in more detail below.

Although FIGS. 1 and 2 depict components in computing device 102 asdistinct, it is to be appreciated that two or more components can beimplemented in a common component. Further, it is to be appreciated thatthe design of the computing device 102 can include other componentselections, component placements, etc., to facilitate automaticallygenerating presentation notes based upon gaze tracking informationassociated with observers of a presentation, and metadata and heuristicsassociated with the presentation in accordance with one or moreembodiments described herein. Moreover, the aforementioned systemsand/or devices have been described with respect to interaction betweenseveral components. It should be appreciated that such systems andcomponents can include those components or sub-components specifiedtherein, some of the specified components or sub-components, and/oradditional components. Sub-components could also be implemented ascomponents communicatively coupled to other components rather thanincluded within parent components. Further yet, one or more componentsand/or sub-components can be combined into a single component providingaggregate functionality. The components can also interact with one ormore other components not specifically described herein for the sake ofbrevity, but known by those of skill in the art.

FIG. 3 illustrates an example, non-limiting, presentation 302 inaccordance with one or more embodiments of the present invention.Presentation 302 contains content elements and includes slide 1 304A to( . . . ) slide N 304B, where N is a positive integer representing thequantity of slides in presentation 302. Additional examples and detailsof Presentation 302, slide content and exemplary embodiment of thepresent invention will be discussed below.

FIG. 4 illustrates an example, non-limiting, slide 402 in accordancewith one or more embodiments of the present invention. For example,slide 402 can represent a slide from presentation 302. Slide 402 caninclude content elements 404, 406, 408, 410, 412, 414, 416, 418, 420,422, and 424. It is to be appreciated that a content element can includeother content elements. For example, content element 410 includescontent elements 412, 414, 416, 418, 420, 422, and 424. A contentelement can include any content that can presented in a presentation302, non-limiting examples of which can include, textual content, imagecontent, audio content, video content, animation content, or any othersuitable content that can be presented in a presentation 302. It is tobe appreciated that while eleven content elements are shown here forillustrative purposes, any suitable number of content elements can beincluded in a slide.

Content component 202 can obtain presentation 302 from a presentationsource, non-limiting example of which can include a presenter, ateacher, a lecturer, an author of the presentation 302, a knowledgebase, a content database, or any other suitable presentation sourceassociated with the presentation 302.

Content component 202 can obtain metadata for respective contentelements of the presentation 302 from the presentation source. In anon-limiting example, metadata can include a content type of the contentelement. Non-limiting example of content types can include, textualcontent, image content, audio content, video content, animation content,or any other suitable content type that can be presented in apresentation 302. Metadata can also include a topic t of the contentelement, which indicates the topic the content elements is associatedwith. Metadata can also include an importance level i of the contentelement, which indicates an importance of the content element forunderstanding the topic t. Metadata can also include an effort level eof the content element, which indicates an amount of effort in terms ofattention of the observer to the content element for understanding thetopic t. Metadata can also include a unique content identifiercontent-id for the content element.

In a non-limiting example, a professor can make a classroom presentationavailable to content component 202 and can provide metadata for contentelements in the classroom presentation. For example, content componentcan provide a user interface (not shown) that allows the professor totag content elements of the presentation with metadata. In anothernon-limiting example, the professor can embed the metadata in apresentation file of the presentation.

Content component 202 can obtain heuristics for the presentation 302and/or respective content elements of the presentation 302 from thepresentation source. In a non-limiting example, heuristics can includepreferences for particular content types for presentation notes. Forexample, a presentation source can specify weights for respectivecontent types indicative of their preference for the respective contenttypes when generating presentation notes. Continuing with the professorexample, the professor can rank content types in order of preference:1—text, 2—video, 3—image. 4—audio, and 5—animation. It is to beappreciated that a presentation source can employ any suitableindication of preferences of content types.

In a non-limiting example, heuristics can also include preferences forparticular sources (e.g., content sources) of content for presentationnotes. For example, a presentation source can specify weights forrespective content sources indicative of their preference for therespective content sources when generating presentation notes. In anon-limiting example, content sources can include, Internet, textbooks,books, articles, white papers, newspapers, blogs, television, movies,radio, massive open online course (MOOC) video, animations, audiorecordings library, knowledge bases, content databases, or any othersuitable source of content for presentation notes. Continuing with theprofessor example, the professor can weight content sources: 50%textbooks, 20% books, 15% knowledge bases. 10% Internet, and 5% MOOC,etc. . . . It is to be appreciated that a presentation source can employany suitable indication of preferences of content sources.

In a further non-limiting example, heuristics can also include anattention function ƒ(a) for determining an attention score a and/or acontent clarity function ƒ(c) for determining content clarity level c,both of which as discussed in more detail below. For example, apresentation source can specify attention function ƒ(a) and/or contentclarity function ƒ(c) that presentation notes component 104 can employfor automatically generating presentation notes.

With reference now also to FIGS. 1-4, presentation notes component 104can also include gaze tracking component 204 that can generate gazetracking information for observers of presentation 302 based on imagescaptured from one or more cameras 114 directed at the observers whilepresentation 302 is presented. For example, in some embodiments, gazetracking component 204 can determine for respective slides ofpresentation 302, gaze tracking information e.g., time stamped gazepoints for an observer. In some embodiments, the time stamped gazepoints can include unique observer identifiers, e.g., for each ofmultiple observers.

FIG. 5 illustrates an example, non-limiting, slide 402 from FIG. 4 withgaze points in accordance with one or more embodiments of the presentinvention. In a non-limiting example, slide 402 can include gaze points502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, and 524. It is tobe appreciated that while twelve gaze points in shown here forillustrative purposes, any suitable number of gaze points can bedetermined by gaze tracking component 204 for a slide, such as in anon-limiting example, thousands or millions of gaze points.

With reference also to FIGS. 1-5, presentation notes component 104 canalso include attention scoring component 206 that can determinerespective attentions scores for content elements on slides of apresentation 302. In some embodiments, attention scoring component 206can determine respective statistics for content elements based on gazetracking information for use by an attention scoring function ƒ(a) todetermine respective attention scores a for content elements. Forexample, attention scoring function ƒ(a) can specify particularstatistics derived from the gaze tracking information that are employedfor determining attention score a.

In a non-limiting example, attention scoring component 206 can determinea statistic for a content element including a number of fixations (NF)of observers for a fixation area associated with the content element. Afixation can refer to a collection of sequential gaze points of anobserver that are within a defined area (e.g., fixation area) andoccurring for at least a defined minimum amount of time. Attentionscoring component 206 can determine fixations based on the gazeinformation. In a non-limiting example, a fixation area can comprise anarea of the slide associated with a content element from which afixation can be determined. For example, if a first observer has afixation on the fixation area once and them looks away and has anotherfixation on the fixation area and then looks away again, and doesn'thave another fixation on the fixation area, then there are two fixationsfor the observer on the fixation area. If there are two observers, andthe second observer has three fixations on the fixation area, then thenumber of fixations (NF) for the content element would be five.

FIG. 6 illustrates an example, non-limiting, slide 402 from FIG. 5 withfixation areas in accordance with one or more embodiments of the presentinvention. As depicted, slide 402 includes fixation areas 602, 604, 606,608, 610, 612, 614, 616, 618, 620, and 622. In this example, fixationareas can correspond to content elements. For example: fixation area 602corresponds to content element 404; fixation area 604 corresponds tocontent element 406; fixation area 606 corresponds to content element408; fixation area 608 corresponds to content element 410; fixation area610 corresponds to content element 412; fixation area 612 corresponds tocontent element 414; fixation area 614 corresponds to content element416; fixation area 616 corresponds to content element 418; fixation area618 corresponds to content element 420; fixation area 620 corresponds tocontent element 422; and fixation area 622 corresponds to contentelement 424. It is to be appreciated that while eleven fixation areasare shown as corresponding to eleven content elements, any suitablenumber (more or less) of fixation areas and/orcombinations/correspondence variations can be employed.

In another non-limiting example, attention scoring component 206 canalso determine another statistic for a content element including a totalfixation duration (TFD) of observers for a fixation area associated withthe content element. The total fixation duration (TFD) for the contentelement includes the total time spent on the fixations of the number offixations (NF) for the content element. Continuing with the two observerexample, if the first observer spent 1 second on the first fixation and3 seconds on the second fixation, and the second observer spent 2seconds on the first fixation, 1 second on the second fixation, and 5seconds on the third fixation, then the total fixation duration (TFD)for the content element would be 12 seconds.

In further non-limiting example, attention scoring component 206 canalso determine another statistic for a content element including anaverage fixation duration (AFD) of observers for a fixation areaassociated with the content element. The average fixation duration (AFD)for the content element can be determined by dividing the total fixationduration (TFD) for the content element by the number of fixations (NF)for the content element.

In an additional non-limiting example, attention scoring component 206can also determine another statistic for a content element including apercentage viewing time (PVT) of observers for a fixation areaassociated with the content element. To determine the percentage viewingtime (PVT) for the content element, attention scoring component 206 candetermine a total time tracked (TTT) for a slide on which the contentelement resides, by adding the total fixation duration (TFD) for allcontent elements on the slide. The percentage viewing time (PVT) for thecontent element can be determined by dividing the total fixationduration (TFD) for the content element by the total time tracked (TTT)for the slide on which the content element resides.

In another non-limiting example, attention scoring component 206 canalso determine another statistic for a content element including acluster average of saccade paths (ASP) of observers for a fixation areaassociated with the content element. A saccade path can refer to atransition path (e.g., a vector in the coordinate space) of anobserver's gaze from one fixation to another fixation. Attention scoringcomponent 206 can determine saccade paths based on the gaze information.Attention scoring component 206 can employ any suitable clusteringalgorithm to cluster saccade paths associated with a content element. Itis to be appreciated that attention scoring component 206 can determinea cluster of saccade paths for a content element based on saccade pathsdirected towards the content element, saccade paths directed away fromthe content element, or both saccade paths directed towards and awayfrom the cluster element. Attention scoring component 206 can determinerespective representative saccade paths for the clusters. In anon-limiting example, a representative saccade path for a cluster can bean average saccade path for the cluster. Attention scoring component 206can generate a cluster average of saccade paths (ASP) for a contentelement which comprises a set of representative saccade paths for theclusters associated with the content element.

FIG. 7 illustrates an example, non-limiting, slide 402 from FIG. 6 withsaccade paths in accordance with one or more embodiments of the presentinvention. As depicted, slide 402 can include saccade paths 702, 704,706, 708, 710, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732,734, 736, 738, 740, 742, and 744. For example, saccade path 702corresponds to a gaze transition from fixation area 602 to fixation area604, saccade path 714 corresponds to a gaze transition from fixationarea 604 to fixation area 608, and saccade path 724 corresponds to agaze transition from fixation area 610 (which is within fixation area608) to fixation area 602. It is to be appreciated that while twenty-twosaccade paths are shown here for illustrative purposes, any suitablenumber of saccade paths can be determined.

Using one or more of the statistics described above and/or otherstatistics or metadata, attention scoring component 206, can determineattention score a for a content element using attention function ƒ(a).It is to be appreciated that attention function ƒ(a) can be specified bya presentation source, predefined in the system, or dynamicallydetermined using artificial intelligence algorithms. For example,attention scoring component 206 can employ deep learning models todetermine an attention function ƒ(a) that is more effective (e.g.,improves or optimizes) for generating presentation notes, such as in anon-limiting example, for a particular set of observers, for aparticular presentation topic, for a particular presentation source, fora particular content type, or using any other suitable criteria.

Attention scoring component 206 can generate respective tuples(contentid, i, e, a) for the content elements that include the uniquecontent identifier content-id for the content element, the importancelevel i of the content element, the effort level e of the contentelement, and the attention score a for the content element.

Presentation notes component 104 can also include content recommendationcomponent 208 that can determine content, from one or more contentsources, for association with content elements and to include inpresentation notes based on a content clarity function ƒ(c) fordetermining respective content clarity levels c for the contentelements. For example, content clarity function ƒ(c) can be a functionbased on the importance level i of the content element, the effort levele of the content element, the attention score a for the content element,and/or the heuristics. It is to be appreciated that content clarityfunction ƒ(c) can be specified by a presentation source, predefined inthe system, or dynamically determined using artificial intelligencealgorithms. For example, content recommendation component 208 can employdeep learning models to determine a content clarity function ƒ(c) thatis more effective for generating presentation notes, such as in anon-limiting example, for a particular set of observers, for aparticular presentation topic, for a particular presentation source, fora particular content type, or using any other suitable criteria.

In a non-limiting example, using the content clarity function ƒ(c),content recommendation component 208 can determine a content claritylevel c for a content element based on a low attention score a for thecontent element, a high importance level i of the content element, and ahigh effort level e of the content element, that would indicate a lowlevel of understanding of the content element and a need to add greaterlevel of content in the presentation notes for the content element, thanan amount of content in the presentation notes for another contentelement with a content clarity level c indicative of a higher level ofunderstanding of the other content element.

Based on the content clarity level c determined for a content element,content recommendation component 208 can determine content to include inthe presentation notes for the content element. For example, based onrespective content clarity levels c determined for content elements in apresentation 302, content recommendation component 208 can determinerespective content to include in generated presentation notes associatedwith presentation 302 for the content elements.

Presentation notes component 104 can also include notes generationcomponent 210 that can generate presentation notes for a presentation302 using respective content selected for content elements ofpresentation 302. In a non-limiting example, notes generation component210 can employ respective content selected by content recommendationcomponent 208 for content elements to generate presentation notes thatfollow an order of the presentation 302. In an example, content selectedby content recommendation component 208 for content elements to can beincorporated directly into presentation 302 to generate presentationnotes. In another example, notes generation component 210 can generate adocument that presents content elements along with content selected bycontent recommendation component 208 for the content elements. In afurther example, the presentation can be ordered by content claritylevels c of the content elements, attention scores a of the contentelements, importance levels i of the content elements, effort levels eof the content elements, or some combination and/or function thereof. Itis to be appreciated that presentation notes can be generated in anysuitable format.

Furthermore, notes generation component 210 can automatically transmitthe generated presentation notes to one or more observers of thepresentations. In addition, notes generation component 210 can cause adevice associated with an observer to display portions of the generatednotes on a display as the observer is reviewing presentation 302. Inanother example, notes generation component 210 can cause a device totrigger an action that draws the attention of observer to a generatedpresentation note in response to determining the observer has a lowlevel of understanding of the content element associated with thepresentation note and a gaze vector associated with the observer doesnot intersect with a portion of a display associated with the contentelement and/or presentation note.

In another example, notes generation component 210 can send atransmission including the generated presentation notes to a device thatinitiates the device to perform an action based on the generatedpresentation notes. For example, notes generation component 210 can senda transmission including the generated presentation notes to a roboticdevice that initiates the robotic device to assist an observerassociated with presentation 302 in learning.

Some embodiments can employ artificial intelligence (AI) to facilitateautomating one or more features described herein. The components canemploy various AI-based schemes for carrying out variousembodiments/examples disclosed herein. In order to provide for or aid inthe numerous determinations (e.g., determine, ascertain, infer,calculate, predict, prognose, estimate, derive, forecast, detect,compute) described herein, components described herein can examine theentirety or a subset of the data to which it is granted access and canprovide for reasoning about or determine states of the system,environment, etc. from a set of observations as captured via eventsand/or data. Determinations can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The determinations can be probabilistic—that is,the computation of a probability distribution over states of interestbased on a consideration of data and events. Determinations can alsorefer to techniques employed for composing higher-level events from aset of events and/or data.

Such determinations can result in the construction of new events oractions from a set of observed events and/or stored event data, whetheror not the events are correlated in close temporal proximity, andwhether the events and data come from one or several event and datasources. Components disclosed herein can employ various classification(explicitly trained (e.g., via training data) as well as implicitlytrained (e.g., via observing behavior, preferences, historicalinformation, receiving extrinsic information, etc.)) schemes and/orsystems (e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines, etc.) inconnection with performing automatic and/or determined action inconnection with the claimed subject matter. Thus, classification schemesand/or systems can be used to automatically learn and perform a numberof functions, actions, and/or determination.

A classifier can map an input attribute vector, z=(z1, z2, z3, z4, zn),to a confidence that the input belongs to a class, as byf(z)=confidence(class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determinate an action to be automaticallyperformed. A support vector machine (SVM) can be an example of aclassifier that can be employed. The SVM operates by finding ahyper-surface in the space of possible inputs, where the hyper-surfaceattempts to split the triggering criteria from the non-triggeringevents. Intuitively, this makes the classification correct for testingdata that is near, but not identical to training data. Other directedand undirected model classification approaches include, e.g., naïveBayes, Bayesian networks, decision trees, neural networks, fuzzy logicmodels, and/or probabilistic classification models providing differentpatterns of independence can be employed. Classification as used hereinalso is inclusive of statistical regression that is utilized to developmodels of priority.

FIG. 8 illustrates an example, non-limiting computer-implemented methodin accordance with one or more embodiments of the present invention.Repetitive description of like elements employed in other embodimentsdescribed herein is omitted for sake of brevity.

At 802, method 800 can comprise obtaining, by a system operativelycoupled to a processor, gaze tracking information associated withobservers of a presentation (e.g., via a gaze tracking component 204, apresentation notes component 104, and/or a computing device 102). At804, method 800 can comprise determining, by the system, respectivecontent clarity scores for content elements of the presentation (e.g.,via a content component 202, an attention scoring component 206, acontent recommendation component 208, a presentation notes component104, and/or a computing device 102). At 806, method 800 can compriseselecting, by the system, respective content from one or more contentsources for the content elements based on the respective content clarityscores (e.g., via a content recommendation component 208, a presentationnotes component 104, and/or a computing device 102). At 808, method 800can comprise generating, by the system, presentation notes based on thepresentation and the selected respective content for the contentelements of the presentation (e.g., via a notes generation component210, a presentation notes component 104, and/or a computing device 102).

FIG. 9 illustrates an example, non-limiting computer-implemented methodin accordance with one or more embodiments of the present invention. Ina non-limiting example, method 900 can be employed at step 804 of method800. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

At 902, method 900 can comprise obtaining, by a system operativelycoupled to a processor, an importance level for a content element of apresentation (e.g., via a content component 202, a presentation notescomponent 104, and/or a computing device 102). At 904, method 900 cancomprise obtaining, by the system, an effort level for the contentelement (e.g., via a content component 202, a presentation notescomponent 104, and/or a computing device 102). At 906, method 900 cancomprise obtaining, by the system, heuristics for the presentation(e.g., via a content component 202, a presentation notes component 104,and/or a computing device 102). At 908, method 900 can comprisedetermining, by the system, statistics for the content element based ongaze tracking information associated with observers of the presentation(e.g., via an attention scoring component 206, a gaze tracking component204, a presentation notes component 104, and/or a computing device 102).At 910, method 900 can comprise determining, by the system, an attentionscore for the content element based on an attention function and thestatistics (e.g., via an attention scoring component 206, a presentationnotes component 104, and/or a computing device 102). At 912, method 900can comprise determining, by the system, a content clarity score for thecontent element based on a content clarity function, the Importancelevel, the effort level, the attention score, and/or the heuristics(e.g., via a content recommendation component 208, a presentation notescomponent 104, and/or a computing device 102).

One or more processes in accordance with the present invention can beperformed by one or more computers (e.g., computer 102) specificallyadapted (or specialized) for carrying out defined tasks related toautomatically generating recommended query terms that are specialized toa topic of desired information based on a query associated with a user.

For simplicity of explanation, computer-implemented methodologies inaccordance with the present invention are depicted and described as aseries of acts. It is to be understood and appreciated that the subjectinnovation is not limited by the acts illustrated and/or by the order ofacts, for example acts can occur in various orders and/or concurrently,and with other acts not presented and described herein. Furthermore, notall illustrated acts can be required to implement thecomputer-implemented methodologies in accordance with the disclosedsubject matter. In addition, those skilled in the art will understandand appreciate that the computer-implemented methodologies couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be further appreciatedthat the computer-implemented methodologies disclosed hereinafter andthroughout this specification are capable of being stored on an articleof manufacture to facilitate transporting and transferring suchcomputer-implemented methodologies to computers. The term article ofmanufacture, as used herein, is intended to encompass a computer programaccessible from any computer-readable device or storage media.

In order to better provide context for various aspects of the invention,FIG. 10, as well as the following discussion, are intended to provide ageneral description of a suitable environment in which the variousaspects of the disclosed subject matter can be implemented. FIG. 10illustrates an example, non-limiting operating environment in accordancewith one or more embodiments of the present invention. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

With reference to FIG. 10, operating environment 1000 can include acomputer 1012. The computer 1012 (similar to the example computingdevice 102 of FIG. 1) can also include a processing unit 1014, a systemmemory 1016, and a system bus 1018. The system bus 1018 operably couplessystem components including, but not limited to, the system memory 1016to the processing unit 1014. The processing unit 1014 can be any ofvarious available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit1014. The system bus 1018 can be any of several types of busstructure(s) including the memory bus or memory controller, a peripheralbus or external bus, and/or a local bus using any variety of availablebus architectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI). The system memory 1016 can alsoinclude volatile memory 1020 and nonvolatile memory 1022. By way ofillustration, and not limitation, nonvolatile memory 1022 can includeread only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable programmable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). The basic input/output system (BIOS),containing the basic routines to transfer information between elementswithin the computer 1012, such as during start-up, can also be stored innonvolatile memory 1022.

Volatile memory 1020 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 1012 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample, a disk storage 1024. Disk storage 1024 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 1024 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 1024 to the system bus 1018, a removableor non-removable interface is typically used, such as interface 1026.

Operating environment 1000 can also include software that acts as anintermediary between users and the basic computer resources described inoperating environment 1000. Such software can also include, for example,an operating system 1028. Operating system 1028, which can be stored ondisk storage 1024, acts to control and allocate resources of thecomputer 1012. Applications 1030 can take advantage of the management ofresources by operating system 1028 through program modules 1032 andprogram data 1034, e.g., stored either in system memory 1016 or on diskstorage 1024. In some embodiments, applications 1030 include one or moreaspects presentation notes component 104 (FIG. 1) and/or embody one ormore of the processes described with reference to FIG. 7 and/or FIG. 8.

It is to be appreciated that this invention can be implemented withvarious operating systems or combinations of operating systems.Referring again to FIG. 10, commands or information can be input to thecomputer 1012 through input device(s) 1036. Examples of input devices1036 include, but are not limited to, a pointing device such as a mouse,trackball, stylus, touch pad, keyboard, microphone, joystick, game pad,satellite dish, scanner, TV tuner card, digital camera, digital videocamera, web camera, and the like. The input devices (and possibly otherdevices) can connect to processing unit 1014 through the system bus 1018via interface port(s) 1038. Interface port(s) 1038 include, for example,a serial port, a parallel port, a game port, and a universal serial bus(USB). Output device(s) 1040 can use some of the same type of ports asinput device(s) 1036. Thus, for example, a USB port can be used toprovide input to computer 1012, and to output information from computer1012 to an output device 1040. Output adapter 1042 is provided toillustrate that there are some output devices 1040 like monitors,speakers, and printers, among other output devices 1040, which requirespecial adapters. The output adapters 1042 include, by way ofillustration and not limitation, video and sound cards that provide ameans of connection between the output device 1040 and the system bus1018. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. The remote computer(s) 1044 can be a computer, a server, a router,a network PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 1012.For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically connected via communication connection 1050. Networkinterface 1048 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN), wide-area networks (WAN), cellularnetworks, etc. LAN technologies include Fiber Distributed Data Interface(FDDI), Copper Distributed Data Interface (CDDI), Ethernet, Token Ringand the like. WAN technologies include, but are not limited to,point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL). Communicationconnection(s) 1050 refers to the hardware/software employed to connectthe network interface 1048 to the system bus 1018. While communicationconnection 1050 is shown for illustrative clarity inside computer 1012,it can also be external to computer 1012. The hardware/software forconnection to the network interface 1048 can also include, for exemplarypurposes only, internal and external technologies such as, modemsincluding regular telephone grade modems, cable modems and DSL modems,ISDN adapters, and Ethernet cards.

In an embodiment, for example, computer 1012 can perform operationscomprising: obtaining gaze information associated with observers of apresentation, determining respective content clarity scores for contentelements of the presentation based on a content clarity function,selecting respective content from one or more content sources for thecontent elements based on the respective content clarity scores, andgenerating presentation notes based on the presentation and the selectedrespective content for the content elements of the presentation.

It is to be appreciated that operations of embodiments disclosed hereincan be distributed across multiple (local and/or remote) systems.

It is also to be understood that some computer processing systems,computer-implemented methods, computer apparatuses, and/or computerprogram products in accordance with the present invention can beemployed to solve new technical problems that arise e.g., throughadvancements in technology, computer networks, the Internet and thelike. Moreover, some computer processing systems, methods apparatusesand/or computer program products in accordance with the presentinvention can provide technical improvements by automatically generatingpresentation notes based upon gaze tracking information associated withobservers of a presentation, and metadata and heuristics associated withthe presentation. A few examples of such improvements include: improvingprocessing efficiency among processing components in applicable systems;reducing delay in processing performed by applicable processingcomponents, and/or improving the accuracy in which the applicablesystems automatically generate presentation notes based upon gazetracking information associated with observers of a presentation, andmetadata and heuristics associated with the presentation.

Embodiments of the present invention may be a system, a method, anapparatus and/or a computer program product at any possible technicaldetail level of integration. The computer program product can include acomputer readable storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outaspects of the present invention. The computer readable storage mediumcan be a tangible device that can retain and store instructions for useby an instruction execution device. The computer readable storage mediumcan be, for example, but is not limited to, an electronic storagedevice, a magnetic storage device, an optical storage device, anelectromagnetic storage device, a semiconductor storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer readable storage medium can alsoinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), a static randomaccess memory (SRAM), a portable compact disc read-only memory (CD-ROM),a digital versatile disk (DVD), a memory stick, a floppy disk, amechanically encoded device such as punch-cards or raised structures ina groove having instructions recorded thereon, and any suitablecombination of the foregoing. A computer readable storage medium, asused herein, is not to be construed as being transitory signals per se,such as radio waves or other freely propagating electromagnetic waves,electromagnetic waves propagating through a waveguide or othertransmission media (e.g., light pulses passing through a fiber-opticcable), or electrical signals transmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of various aspects of thepresent invention can be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions can executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer can be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection can be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) can execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to customize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this invention also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this invention can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a servercomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisinvention, terms such as “store,” “storage,” “data store,” datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems, computerprogram products, and computer-implemented methods. It is, of course,not possible to describe every conceivable combination of components,products and/or computer-implemented methods for purposes of describingthis invention, but one of ordinary skill in the art can recognize thatmany further combinations and permutations of this invention arepossible. Furthermore, to the extent that the terms “includes,” “has,”“possesses,” and the like are used in the detailed description, claims,appendices and drawings such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim. The descriptions of thevarious embodiments have been presented for purposes of illustration,but are not intended to be exhaustive or limited to the embodimentsdisclosed. Many modifications and variations will be apparent to thoseof ordinary skill in the art without departing from the scope and spiritof the described embodiments. The terminology used herein was chosen tobest explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A computer-implemented method, comprising:obtaining, by a system including one or more processors, gazeinformation associated with observers of a presentation; determining, bythe system, respective attention scores for content elements of thepresentation based on the gaze information, wherein the determiningcomprises determining an attention score for a content element of thecontent elements based on an attention function that employs a clusteraverage of saccade paths for the content element determined based on thegaze information, wherein the cluster average of saccade paths comprisesa set of respective representative saccade paths for clusters of saccadepaths associated with the content element; determining, by the system,respective content clarity scores for content elements of thepresentation based on a content clarity function and the gazeinformation, wherein the content clarity function employs the attentionscore for the content element; selecting, by the system, respectivecontent, from one or more content sources, for the content elementsbased on the respective content clarity scores; and generating, by thesystem, presentation notes based on the presentation and the selectedrespective content for the content elements of the presentation.
 2. Thecomputer-implemented method of claim 1, further comprising controlling,by the system, a device associated with an observer to display thepresentation notes as the observer is reviewing the presentation.
 3. Thecomputer-implemented method of claim 1, wherein the attention functionfurther employs one or more statistics selects from a group consistingof a total fixation duration for the content element, a number offixations for the content element, an average fixation duration for thecontent element, and a percentage viewing time for the content element.4. The computer-implemented method of claim 1, further comprisingobtaining, by the system, respective importance levels for the contentelements, wherein the content clarity function further employs animportance level for the content element.
 5. The computer-implementedmethod of claim 1, further comprising obtaining, by the system,respective effort levels for the content elements, wherein the contentclarity function further employs an effort level for the contentelement.
 6. The computer-implemented method of claim 1, furthercomprising obtaining, by the system, a heuristic for the contentelement, wherein the content clarity function further employs theheuristic for the content element.
 7. The computer-implemented method ofclaim 1, further comprising determining, by the system, the contentclarity function based upon a deep learning model that is optimized forlearning for a particular topic of the presentation.